Crash Modification Functions for Passing Relief Lanes on Two-Lane Rural Roads
Bibliographic record
Abstract
Passing relief lanes on two-lane rural roads provide passing opportunities that would otherwise be scarce where there are extensive no-passing zones, high opposing traffic volumes, or both. This paper addresses the safety effects of installing a passing lane or lengthening an existing one. It stands to reason that the effect of installing a passing lane will depend on the actual length of that lane. By extension, it is also reasonable to expect that the safety effects of lengthening an existing one will depend not only on the amount of the lengthening, but also on the original length. Yet, knowledge that can be applied to estimate these two sets of effects in a design process is lacking. The crash modification factors (CMFs) in the Highway Safety Manual (HSM) and in the CMF Clearinghouse for installing a passing lane are all single-valued, of the order of 0.75. And neither source provides CMFs for lengthening an existing passing lane. This paper seeks to address these voids by developing continuous crash modification functions (CMFunctions) for both sets of design decisions using Michigan, U.S., and Ontario, Canada, crash, geometric, and traffic data for passing lane and reference sections. Generalized linear modeling and full Bayes Markov Chain Monte Carlo (FB MCMC) simulation are used to develop cross-section regression models from which crash modification functions are derived and compared. The results are consistent with those from credible before-after studies, so are recommended for implementation in practice, in particular for HSM applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".